Learning initial trajectory using sequence-to-sequence approach to warm start an optimization-based motion planner

In recent years, optimization-based motion planners have shown that they can provide a fast, smooth, and locally optimal trajectory even for a higher dimension planning problem. Their convergence rate depends on the given initial trajectory. The proper selection of an initial trajectory is crucially important: if it is not within the basin of attraction of the optimum, it will take longer to convergence or even get stuck in local minima. This paper presents a neural network-based initial trajectory predictor, which utilizes the power of the sequence-to-sequence (Seq2Seq) learning method to predict a good initial trajectory for an optimization-based motion planner even in an unseen environment. The proposed model learns the mapping between the tasks and the optimal trajectories from a database. Given a start and a goal configuration of a manipulator along with the environment information in the form of a voxel grid, the proposed model predicts a good initial trajectory, which was learned from previously seen situations. The learned model is evaluated in a 6 degree of freedom (DOF) manipulator planning in two different environments. The results show that by using the predicted initial trajectory, there is a significant improvement in the convergence rate and the planning time of an optimization-based motion planner, even in an unseen environment.